Derivatives of Markov Kernels and Their Jordan Decomposition
نویسنده
چکیده
We study a particular class of transition kernels that stems from differentiating Markov kernels in the weak sense. Sufficient conditions are established for this type of kernels to admit a Jordan-type decomposition. The decomposition is explicitly constructed.
منابع مشابه
Simulation and prediction of land use and land cover change using GIS, remote sensing and CA-Markov model
This study analyzes the characteristics of land use/land cover change in Jordan’s Irbid governorate, 1984–2018, and predicts future land use/land cover for 2030 and 2050 using a cellular automata-Markov model. The results inform planners and decision makers of past and current spatial dynamics of land use/land cover change and predicted urban expansion, for a better understanding and successful...
متن کاملAccelerated decomposition techniques for large discounted Markov decision processes
Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorith...
متن کاملMarkov Chain Analogue Year Daily Rainfall Model and Pricing of Rainfall Derivatives
In this study we model the daily rainfall occurrence using Markov Chain Analogue Yearmodel (MCAYM) and the intensity or amount of daily rainfall using three different probability distributions; gamma, exponential and mixed exponential distributions. Combining the occurrence and intensity model we obtain Markov Chain Analogue Year gamma model (MCAYGM), Markov Chain Analogue Year exponentia...
متن کاملMeasure-Valued Differentiation for Stochastic Processes: The Finite Horizon Case
This paper addresses the problem of sensitivity analysis for finite horizon performance measures of general Markov chains. We derive closed form expressions and associated unbiased gradient estimators for derivatives of finite products of Markov kernels by measure-valued differentiation (MVD). In the MVD setting, derivatives of Markov kernels, called D-derivatives, are defined with respect to a...
متن کاملInfinite-dimensional versions of the primary, cyclic and Jordan decompositions
The famous primary and cyclic decomposition theorems along with the tightly related rational and Jordan canonical forms are extended to linear spaces of infinite dimensions with counterexamples showing the scope of extensions.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008